Why identify plumes and blooms? Cyanobacteria blooms are one of the most significant management challenges in the Great Lakes today. Recurring blooms of varying toxicity are commonly observed in four of the Great Lakes, and the fifth, Lake Superior, has experienced intermittent nearshore blooms since 2012. The recent advent of cyanobacterial blooms in Lake Superior is disconcerting, given the highly valued, pristine water quality of the large lake. Many fear the appearance of blooms portend a very different future for Lake Superior. As a public resource, the coastal water quality of Lake Superior has tremendous economic, public health, and environmental value, and therefore, preventing cyanobacterial blooms in Lake Superior is a high-priority management challenge.
Lake Superior is a large lake, and relying on human observations of blooms restricts observations to near-shore locations. Remote sensing has the potential to catalog spatial and temporal extent of surface blooms. In this project, we are attempting to use optical imagery from Lake Superior to delineate surface plumes (sediment) and blooms (algae). It is likely that these two surface features occur at the same time (i.e a rainstorm may lead to a sediment plume from a river and subsequently an algal boom).
To train computer algorithms to detect these features in satellite images we need a training dataset. That’s where we need your help! In this exercise, we ask you to participate in identify changes in surface conditions in the western arm of Lake Superior. All you need is a computer and your eyes.
We will be using Google Earth Engine (GEE) for this project and instructions on how to use this software are detailed below.
If this is your first time using GEE and this classification workflow, please follow the tutorial below to have your account and permissions setup appropriately. You should only need to do this step once.
You can also watch the first 2.5 minutes of this video to visually walk through the setup instructions. Note that the video was originally created for the “GROD workflow”, which was the foundation for the workflow here, and you may need to substitute information that is specific for this project.
flow chart & examples
The purpose of this step is to walk you through how to setup a script in GEE to be able to open an image before you actually label it. You will need to do this process before each new mission-date combination you are classifying.
Go to the project Github page.
Copy the appropriate .js script.
eePlumB_validation.js.
The purpose of this step is to have you go through the full process of
classifying a few images for practice and to provide us with data to see
how similarly all our volunteers classify a range of conditions. We hope
that the examples below will help guide everyone to classify conditions
similarly, but we understand that there will be variation as there is a
level of subjectivity in this process.eePlumB_classification.jsvar openWater =... and GEE will prompt you to import
these records. Click convert to do so.eePlumB_[YOUR INITIALS]_validation.eePlumB_[YOUR INITIALS]_[MISSION]_[DATE]
based on the assigned mission-date combination you are currently working
through.Now that you have the script running, you are ready to add labels.
Head to the next section, How to label: Part 2!
The purpose of this step is to teach you how to use our Google Earth
Engine framework to classify images. Hopefully, this section is an easy
reference if you need a refresher in the future. Note that this step
assumes you have already done How to label: Part 1 for the
current set of images (either validation or for the specific
mission-date you have been assigned).
Geometry Imports in the map area of GEE. When
hovering, you should see a list of sediment and bloom types (see image
below). If you do not, then you should revisit Part 1 and follow those
instructions carefully.Change the category to the type of pixel that you are labeling by
hovering over openWater (it just chooses the first category
by default) and clicking the name of the category you want.
Next, click on the map to add a point of this type.
Continue adding points in the current category by clicking the pixel on the map that you want to label. Zoom in or out and scroll (by clicking and dragging) as needed. For each image, label at least 5 pixels for each category you see. Note that you may not see all categories in a given image.
When you are done, click Exit next to where it says
“Point drawing”. If you want to start adding points again, simply go
back to Step 2 and repeat.
If you have made a mistake, click on the hand icon at the top
left-hand corner of the map (see the red circle on the figure below).
Next, click on the point you dislike. Then, you can drag to a new
location or hit delete to remove the point. If you moved the point, hit
Exit to stop the editing session.
To resume adding points, go back to Step 2 in the previous section.
When you have completed labeling your image, …
Here are some useful shortcuts if you are a keyboard rather than mouse person.